mirror of
https://github.com/Steffo99/unimore-bda-6.git
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188 lines
6.2 KiB
Python
188 lines
6.2 KiB
Python
"""
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This file is a vendored version of `Christopher Potts' tokenizer <http://sentiment.christopherpotts.net/tokenizing.html>`_, which the project's specifications require to use.
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It has been altered to be used with Python 3.10, but the code is mostly the same.
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=========================
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Original module docstring
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=========================
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This code implements a basic, Twitter-aware tokenizer.
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A tokenizer is a function that splits a string of text into words. In
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Python terms, we map string and unicode objects into lists of unicode
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objects.
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There is not a single right way to do tokenizing. The best method
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depends on the application. This tokenizer is designed to be flexible
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and this easy to adapt to new domains and tasks. The basic logic is
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this:
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1. The tuple regex_strings defines a list of regular expression
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strings.
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2. The regex_strings strings are put, in order, into a compiled
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regular expression object called word_re.
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3. The tokenization is done by word_re.findall(s), where s is the
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user-supplied string, inside the tokenize() method of the class
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Tokenizer.
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4. When instantiating Tokenizer objects, there is a single option:
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preserve_case. By default, it is set to True. If it is set to
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False, then the tokenizer will downcase everything except for
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emoticons.
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The __main__ method illustrates by tokenizing a few examples.
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I've also included a Tokenizer method tokenize_random_tweet(). If the
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twitter library is installed (http://code.google.com/p/python-twitter/)
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and Twitter is cooperating, then it should tokenize a random
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English-language tweet.
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"""
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__author__ = "Christopher Potts"
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__copyright__ = "Copyright 2011, Christopher Potts"
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__credits__ = []
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__license__ = "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License: http://creativecommons.org/licenses/by-nc-sa/3.0/"
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__version__ = "1.0"
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__maintainer__ = "Christopher Potts"
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__email__ = "See the author's website"
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######################################################################
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import re
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import html.entities
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import typing as t
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######################################################################
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# The following strings are components in the regular expression
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# that is used for tokenizing. It's important that phone_number
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# appears first in the final regex (since it can contain whitespace).
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# It also could matter that tags comes after emoticons, due to the
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# possibility of having text like
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#
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# <:| and some text >:)
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#
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# Most imporatantly, the final element should always be last, since it
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# does a last ditch whitespace-based tokenization of whatever is left.
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# This particular element is used in a couple ways, so we define it
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# with a name:
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emoticon_string = r"""
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(?:
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[<>]?
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[:;=8] # eyes
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[\-o\*\']? # optional nose
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[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
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[\)\]\(\[dDpP/\:\}\{@\|\\] # mouth
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[\-o\*\']? # optional nose
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[:;=8] # eyes
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[<>]?
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)"""
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# The components of the tokenizer:
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regex_strings = (
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# Phone numbers:
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r"""
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(?:
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(?: # (international)
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\+?[01]
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[\-\s.]*
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)?
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(?: # (area code)
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[\(]?
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\d{3}
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[\-\s.\)]*
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)?
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\d{3} # exchange
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[\-\s.]*
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\d{4} # base
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)"""
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,
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# Emoticons:
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emoticon_string
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,
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# HTML tags:
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r"""<[^>]+>"""
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,
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# Twitter username:
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r"""(?:@[\w_]+)"""
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,
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# Twitter hashtags:
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r"""(?:\#+[\w_]+[\w\'_\-]*[\w_]+)"""
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,
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# Remaining word types:
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r"""
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(?:[a-z][a-z'\-_]+[a-z]) # Words with apostrophes or dashes.
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(?:[+\-]?\d+[,/.:-]\d+[+\-]?) # Numbers, including fractions, decimals.
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(?:[\w_]+) # Words without apostrophes or dashes.
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(?:\.(?:\s*\.){1,}) # Ellipsis dots.
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(?:\S) # Everything else that isn't whitespace.
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"""
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)
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######################################################################
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# This is the core tokenizing regex:
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word_re = re.compile(r"""(%s)""" % "|".join(regex_strings), re.VERBOSE | re.I | re.UNICODE)
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# The emoticon string gets its own regex so that we can preserve case for them as needed:
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emoticon_re = re.compile(regex_strings[1], re.VERBOSE | re.I | re.UNICODE)
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# These are for regularizing HTML entities to Unicode:
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html_entity_digit_re = re.compile(r"&#\d+;")
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html_entity_alpha_re = re.compile(r"&\w+;")
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amp = "&"
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######################################################################
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class Tokenizer:
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def __init__(self, preserve_case=False):
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self.preserve_case = preserve_case
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def tokenize(self, s: str) -> t.Iterable[str]:
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"""
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Argument: s -- any string object
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Value: a tokenize list of strings; conatenating this list returns the original string if preserve_case=False
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"""
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# Fix HTML character entitites:
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s = self.__html2string(s)
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# Tokenize:
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words = word_re.findall(s)
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# Possible alter the case, but avoid changing emoticons like :D into :d:
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if not self.preserve_case:
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words = map((lambda x : x if emoticon_re.search(x) else x.lower()), words)
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return words
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def __html2string(self, s: str) -> str:
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"""
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Internal metod that seeks to replace all the HTML entities in
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s with their corresponding unicode characters.
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"""
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# First the digits:
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ents = set(html_entity_digit_re.findall(s))
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if len(ents) > 0:
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for ent in ents:
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entnum = ent[2:-1]
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try:
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entnum = int(entnum)
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s = s.replace(ent, chr(entnum))
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except:
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pass
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# Now the alpha versions:
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ents = set(html_entity_alpha_re.findall(s))
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ents = filter((lambda x : x != amp), ents)
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for ent in ents:
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entname = ent[1:-1]
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try:
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s = s.replace(ent, chr(html.entities.name2codepoint[entname]))
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except:
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pass
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s = s.replace(amp, " and ")
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return s
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